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Updated: Oct 8, 2026
Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke
Published on: June 2, 2023
Parameter-free dual-student ensemble for semi-supervised ischemic stroke segmentation on follow-up NCCT
Yuqing Sha1, Long Wang1, Chao Huang1
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Introduction:
Non-contrast computed tomography (NCCT) is widely used for post-treatment monitoring of patients with ischemic stroke due to its rapid acquisition, low cost, and non-invasiveness. Segmentation of ischemic lesions on NCCT images is critical for assessing disease severity and guiding clinical decision-making.
Methods:
We propose an ensemble-based semi-supervised segmentation (ESS) framework to reduce the dependence of convolutional neural networks (CNNs) on a large number of manual annotations. The proposed method mainly consists of two components: first, strong data augmentation is introduced to increase sample diversity and alleviate overfitting in the presence of limited labeled data; second, a parameter-free ensemble module is adopted to generate more stable supervision signals for unlabeled samples and improve the reliability of pseudo-labels by fusing the predictions of the two student models. The proposed ESS is evaluated on a manually annotated follow-up NCCT dataset consisting of 9,020 image slices.
Results:
ESS consistently achieves the highest Dice coefficient among all compared methods under all four labeled-data ratios, with a more pronounced performance advantage when labeled data are scarce.
Discussion:
These results demonstrate the effectiveness of ESS for semi-supervised segmentation of follow-up NCCT under limited annotation conditions.
